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MindTopo: Can Foundation Models Reason in Topological Space?

Research Multimodal & Generative

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Merged summary

TL;DR - MindTopo is an 11,030-instance benchmark testing foundation models’ topological reasoning and closed-loop planning across continuity, separation, order, enclosure, and knots. Fourteen multimodal LLMs substantially trail humans, especially when planning requires preserving topology across actions.

  • The benchmark spans 13 procedurally generated task types with controllable difficulty and evaluates both reasoning and agentic planning.
  • Every tested multimodal LLM performed better on reasoning than planning; even the strongest remained far below observed human performance.
  • Supervised fine-tuning and reinforcement learning improved Qwen3-VL-2B-Instruct’s reasoning more than its planning.
  • Image- and video-generated observations preserved local cues and plausible endpoints but often violated environment dynamics or topology between transitions.

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MindTopo: Can Foundation Models Reason in Topological Space?

arXiv cs.AI Yunfei Ge, Anbang Liu, Qineng Wang, Johnalbert Garnica, Jianwen Lyu, Zihan Wang, Reuben Tan, Jianfeng Gao, Ruohan Zhang, Yining Hong, Jiajun Wu, Manling Li 2026-09-10 arXiv:2609.11900
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Providers: Hugging Face · Upvotes 0 OpenAlex · N/A Publisher · N/A Semantic Scholar · N/A X · N/A Fetched 2026-09-25 14:20:59.550581 UTC

TL;DR - MindTopo is an 11,030-instance benchmark testing foundation models’ topological reasoning and closed-loop planning across continuity, separation, order, enclosure, and knots. Fourteen multimodal LLMs substantially trail humans, especially when planning requires preserving topology across actions.

  • The benchmark spans 13 procedurally generated task types with controllable difficulty and evaluates both reasoning and agentic planning.
  • Every tested multimodal LLM performed better on reasoning than planning; even the strongest remained far below observed human performance.
  • Supervised fine-tuning and reinforcement learning improved Qwen3-VL-2B-Instruct’s reasoning more than its planning.
  • Image- and video-generated observations preserved local cues and plausible endpoints but often violated environment dynamics or topology between transitions.
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